What is R Markdown? from RStudio, Inc. on Vimeo.
https://bookdown.org/yihui/rmarkdown/document-templates.html
inside R
markdown::markdownToHTML('markdown_example.md',
'markdown_example.html')
command line
R -e "markdown::markdownToHTML('markdown_example.md',
'markdown_example.html')"
command line
export PATH=$PATH:/Applications/RStudio.app/Contents/MacOS/pandoc
R -e "rmarkdown::render('markdown_example.md')"
chunk içinde R kodlarını çalıştırma{r, results='asis'}
iris %>%
tibble::as_tibble() %>%
details::details(summary = 'tibble')R kodlarını çalıştırma{r global_options, include=FALSE}
knitr::opts_chunk$set(fig.width = 12,
fig.height = 8,
fig.path = 'Figs/',
echo = FALSE,
warning = FALSE,
message = FALSE,
error = FALSE,
eval = TRUE,
tidy = TRUE,
comment = NA)
{r}
data("cancer")
cancer
foreign::write.foreign(df = cancer,
datafile = "data/cancer.sav",
codefile = "data/cancer.spo",
package = "SPSS"
){r}
suppressPackageStartupMessages(library("tidyverse"))
suppressPackageStartupMessages(library("survival")){tidyverse} {tidylog}
{lubridate} {janitor}
{readxl} {foreign}
{summarytools} {ggstatsplot} {tangram} {finalfit} {psycho} {jmv}
{survival} {survminer}
{report} {kableExtra}
{r}
View(mydata)
glimpse(mydata){r}
mydata <- janitor::clean_names(mydata)
{r}
mydata$sontarih <- janitor::excel_numeric_to_date(
as.numeric(mydata$olum_tarihi)
){r}
mydata$Outcome <- "Dead"
mydata$Outcome[mydata$olum_tarihi == "yok"] <- "Alive"
{r}
## Recoding mydata$cinsiyet into mydata$Cinsiyet
mydata$Cinsiyet <- recode(mydata$cinsiyet,
"K" = "Kadin",
"E" = "Erkek")
mydata$Cinsiyet <- factor(mydata$Cinsiyet){r recode TNM stage}
#pT2N0Mx -> 2
mydata$Tstage <- stringr::str_match(
mydata$patolojik_evre,
paste('(.+)', "N", sep=''))[,2]
){r recode TNM2}
mydata <- mydata %>%
mutate(
T_stage = case_when(
grepl(pattern = "T1", x = .$Tstage) == TRUE ~ "T1",
grepl(pattern = "T2", x = .$Tstage) == TRUE ~ "T2",
grepl(pattern = "T3", x = .$Tstage) == TRUE ~ "T3",
grepl(pattern = "T4", x = .$Tstage) == TRUE ~ "T4",
TRUE ~ "Tx"
)
){r}
mydata <- mydata %>%
mutate(
TumorPDL1gr1 = case_when(
t_pdl1 < 1 ~ "kucuk1",
t_pdl1 >= 1 ~ "buyukesit1"
)
){r}
library(summarytools)
view(dfSummary(colon_s)){r, results='asis'}
# cat(names(mydata), sep = " + \n")
library(arsenal)
tab1 <- tableby(~ Cinsiyet +
Yas +
TumorYerlesimi
,
data = mydata)
summary(tab1)
tangram: The Grammar of Tables
Easily generate information-rich, publication-quality tables from R
{r}
mydata %>%
janitor::tabyl(Categorical) %>%
adorn_pct_formatting(rounding = 'half up',
digits = 1) %>%
knitr::kable()
{r crosstable}
mydata %>%
summary_factorlist(dependent = dependent,
explanatory = explanatory,
total_col = TRUE,
p = TRUE,
add_dependent_label = TRUE) -> table
knitr::kable(table, row.names = FALSE, align = c('l', 'l', 'r', 'r', 'r'))
{r ggstatplot, layout='l-page'}
mydata %>%
ggstatsplot::ggbarstats(data = .,
main = Categorical_variable,
condition = dependent_variable
)
{r}
mydata %>%
jmv::descriptives(
data = .,
vars = c(yas),
hist = TRUE,
dens = TRUE,
box = TRUE,
violin = TRUE,
dot = TRUE,
mode = TRUE,
sd = TRUE,
variance = TRUE,
skew = TRUE,
kurt = TRUE,
quart = TRUE)
{r crosstable}
library(finalfit)
mydata %>%
summary_factorlist(dependent = dependent,
explanatory = explanatory,
column = TRUE,
total_col = TRUE,
p = TRUE,
add_dependent_label = TRUE,
na_include=FALSE
# catTest = catTestfisher
) -> table
knitr::kable(table,
row.names = FALSE,
align = c('l', 'l', 'r', 'r', 'r')){r define survival time}
mydata$int <- lubridate::interval(
lubridate::ymd(mydata$CerrahiTarih),
lubridate::ymd(mydata$SonTarih)
)
mydata$OverallTime <- lubridate::time_length(mydata$int, "month")
mydata$OverallTime <- round(mydata$OverallTime, digits = 1)
{r}
## Recoding mydata$Outcome into mydata$Outcome2
mydata$Outcome2 <- recode(mydata$Outcome,
"Alive" = "0",
"Dead" = "1")
mydata$Outcome2 <- as.numeric(mydata$Outcome2)
{r Kaplan-Meier}
mydata %>%
finalfit::surv_plot(dependent,
explanatory,
xlab='Time (months)',
pval=TRUE,
legend = 'none',
break.time.by = 12,
xlim = c(0,60),
legend.labs = c('a','b')
)
{r}
km_fit <- survfit(dependent ~ explanatory,
data = mydata)
km_fit
{r, eval=FALSE, include=FALSE}
library(survival)
km <- with(mydata, Surv(OverallTime, Outcome2))
# head(km,80)
# plot(km)
{r 1-3-5-yr}
summary(km_fit, times = c(12,36,60))
{r}
survminer::pairwise_survdiff(formula = Surv(time, Outcome) ~ Group,
data = mydata,
p.adjust.method = "BH")
{r Multivariate Analysis, eval=FALSE, include=FALSE}
library(finalfit)
library(survival)
explanatoryMultivariate <- explanatoryKM
dependentMultivariate <- dependentKM
mydata %>%
finalfit(dependentMultivariate, explanatoryMultivariate) -> tMultivariate
knitr::kable(tMultivariate, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))
Rj Editor – Analyse your data with R in jamovi
Docker Containers for the R Environment
docker run --rm -ti rocker/r-base
Or get started with an RStudio® instance:
docker run -e PASSWORD=yourpassword --rm -p 8787:8787 rocker/rstudio
and point your browser to localhost:8787 Log in with user/password rstudio/yourpassword
{r load library}
source(file = here::here("R", "loadLibrary.R"))
{r}
saved data after analysis to `mydata.xlsx`.
save.image(file = here::here("data", "mydata_work_space.RData"))
readr::write_rds(x = mydata, path = here::here("data", "mydata_afteranalysis.rds"))
saveRDS(object = mydata, file = here::here("data", "mydata.rds"))
writexl::write_xlsx(mydata, here::here("data", "mydata.xlsx"))
paste0(rownames(file.info(here::here("data", "mydata.xlsx"))), " : ", file.info(here::here("data", "mydata.xlsx"))$ctime)
{r github push}
CommitMessage <- paste("updated on ", Sys.time(), sep = "")
wd <- getwd()
gitCommand <- paste("cd ",
wd,
" \n git add . \n git commit --message '",
CommitMessage,
"' \n git push origin master \n",
sep = ""
)
system(command = gitCommand,
intern = TRUE
)
CommitMessage <- paste("updated on ", Sys.time(), sep = "")
wd <- getwd()
gitCommand <- paste("cd ", wd, " \n git add . \n git commit --message '", CommitMessage,
"' \n git push origin master \n", sep = "")
system(command = gitCommand, intern = TRUE)[1] "[master 7785565] updated on 2019-10-01 09:02:22"
[2] " 4 files changed, 85 insertions(+), 180 deletions(-)"
[3] " rename ttttable.R => R/ttttable.R (100%)"
attr(,"status")
[1] 128
{r}
citation()
To cite R in publications use:
R Core Team (2019). R: A language and environment for
statistical computing. R Foundation for Statistical Computing,
Vienna, Austria. URL https://www.R-project.org/.
A BibTeX entry for LaTeX users is
@Manual{,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2019},
url = {https://www.R-project.org/},
}
We have invested a lot of time and effort in creating R, please
cite it when using it for data analysis. See also
'citation("pkgname")' for citing R packages.
Ewen Harrison, Tom Drake and Riinu Ots (2019). finalfit: Quickly Create Elegant Regression Results Tables and Plots when Modelling. R package version 0.9.5. https://CRAN.R-project.org/package=finalfit H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
Hadley Wickham (2017). tidyverse: Easily Install and Load the ‘Tidyverse’. R package version 1.2.1. https://CRAN.R-project.org/package=tidyverse
Hadley Wickham (2019). forcats: Tools for Working with Categorical Variables (Factors). R package version 0.4.0. https://CRAN.R-project.org/package=forcats
Hadley Wickham (2019). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.4.0. https://CRAN.R-project.org/package=stringr
Hadley Wickham and Lionel Henry (2019). tidyr: Tidy Messy Data. R package version 1.0.0. https://CRAN.R-project.org/package=tidyr
Hadley Wickham, Jim Hester and Romain Francois (2018). readr: Read Rectangular Text Data. R package version 1.3.1. https://CRAN.R-project.org/package=readr
Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2019). dplyr: A Grammar of Data Manipulation. R package version 0.8.3. https://CRAN.R-project.org/package=dplyr
Kirill Müller and Hadley Wickham (2019). tibble: Simple Data Frames. R package version 2.1.3. https://CRAN.R-project.org/package=tibble
Lionel Henry and Hadley Wickham (2019). purrr: Functional Programming Tools. R package version 0.3.2. https://CRAN.R-project.org/package=purrr
Therneau T (2015). A Package for Survival Analysis in S. version2.38, <URL: https://CRAN.R-project.org/package=survival>.
{r library citations}
citation("tidyverse")
citation("readxl")
citation("janitor")
citation("report")
citation("finalfit")
citation("ggstatplot")
The jamovi project (2019). jamovi. (Version 0.9) [Computer Software]. Retrieved from https://www.jamovi.org.
R Core Team (2018). R: A Language and envionment for statistical computing. [Computer software]. Retrieved from https://cran.r-project.org/.
Fox, J., & Weisberg, S. (2018). car: Companion to Applied Regression. [R package]. Retrieved from https://cran.r-project.org/package=car.
{r session info, echo=TRUE}
sessionInfo()R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] finalfit_0.9.5 survival_2.44-1.1 forcats_0.4.0
[4] stringr_1.4.0 dplyr_0.8.3 purrr_0.3.2
[7] readr_1.3.1 tidyr_1.0.0 tibble_2.1.3
[10] ggplot2_3.2.1 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] nlme_3.1-141 bitops_1.0-6 matrixStats_0.55.0
[4] lubridate_1.7.4 RColorBrewer_1.1-2 httr_1.4.1
[7] tools_3.6.0 backports_1.1.4 R6_2.4.0
[10] rpart_4.1-15 Hmisc_4.2-0 lazyeval_0.2.2
[13] colorspace_1.4-1 jomo_2.6-9 nnet_7.3-12
[16] withr_2.1.2 tidyselect_0.2.5 gridExtra_2.3
[19] compiler_3.6.0 cli_1.1.0 rvest_0.3.4
[22] formatR_1.7 htmlTable_1.13.2 mice_3.6.0
[25] xml2_1.2.2 scales_1.0.0 checkmate_1.9.4
[28] digest_0.6.21 foreign_0.8-72 minqa_1.2.4
[31] rmarkdown_1.15 base64enc_0.1-3 pkgconfig_2.0.3
[34] htmltools_0.3.6 lme4_1.1-21 htmlwidgets_1.3
[37] rlang_0.4.0 readxl_1.3.1 rstudioapi_0.10
[40] xaringan_0.12 pryr_0.1.4 generics_0.0.2
[43] jsonlite_1.6 acepack_1.4.1 RCurl_1.95-4.12
[46] magrittr_1.5 rapportools_1.0 Formula_1.2-3
[49] Matrix_1.2-17 Rcpp_1.0.2 munsell_0.5.0
[52] lifecycle_0.1.0 stringi_1.4.3 yaml_2.2.0
[55] MASS_7.3-51.4 plyr_1.8.4 grid_3.6.0
[58] promises_1.0.1 parallel_3.6.0 crayon_1.3.4
[61] mitml_0.3-7 lattice_0.20-38 haven_2.1.1
[64] splines_3.6.0 pander_0.6.3 summarytools_0.9.4
[67] hms_0.5.1 magick_2.2 zeallot_0.1.0
[70] knitr_1.25 pillar_1.4.2 tcltk_3.6.0
[73] boot_1.3-23 revealjs_0.9 codetools_0.2-16
[76] pan_1.6 servr_0.15 glue_1.3.1
[79] evaluate_0.14 latticeExtra_0.6-28 data.table_1.12.2
[82] renv_0.6.0-141 modelr_0.1.5 httpuv_1.5.2
[85] vctrs_0.2.0 nloptr_1.2.1 cellranger_1.1.0
[88] gtable_0.3.0 assertthat_0.2.1 xfun_0.9
[91] mime_0.7 broom_0.5.2 later_0.8.0
[94] cluster_2.1.0
https://sbalci.github.io/MyRCodesForDataAnalysis/R-Markdown.nb.html https://sbalci.github.io/MyRCodesForDataAnalysis/R-Markdown.html
Completed on 2019-10-01.
Serdar Balci, MD, Pathologist
drserdarbalci@gmail.com
https://rpubs.com/sbalci/CV
https://sbalci.github.io/
https://github.com/sbalci
https://twitter.com/serdarbalci
https://andrewbtran.github.io/NICAR/2018/workflow/docs/02-rmarkdown.html
https://smithcollege-sds.github.io/sds-public/rmarkdown_problems.html
http://kbroman.org/knitr_knutshell/pages/Rmarkdown.html
https://kbroman.org/knitr_knutshell/pages/overview.html
https://kbroman.org/knitr_knutshell/pages/Rmarkdown.html
https://kbroman.org/knitr_knutshell/pages/markdown.html
csv {headers: true, title: "**Drawing Tables In Markdown**"}
Name, Surname, Known As, Age
Marcelo, David, coldzera, 22
Oleksandr, Kostyliev, s1mple, 19
Nikola, Kovač, NiKo, 20
Richard, Papillon, shox, 25
Nicolai, Reedtz, dev1ce, 21
{pgn}
[Event "Bled-Zagreb-Belgrade Candidates"]
[Site "Bled, Zagreb & Belgrade YUG"]
[Date "1959.10.11"]
[Round "20"]
[Result "1-0"]
[White "Mikhail Tal"]
[Black "Robert James Fischer"]
1. d4 Nf6 2. c4 g6 3. Nc3 Bg7 4. e4 d6 5.
Be2 O-O 6. Nf3 e5 7. d5 Nbd7 8. Bg5 h6 9.
Bh4 a6 10. O-O Qe8 11. Nd2 Nh7 12. b4 Bf6
13. Bxf6 Nhxf6 14. Nb3 Qe7 15. Qd2 Kh7 16.
Qe3 Ng8 17. c5 f5 18. exf5 gxf5 19. f4 exf4
20. Qxf4 dxc5 21. Bd3 cxb4 22. Rae1 Qf6 23.
Re6 Qxc3 24. Bxf5+ Rxf5 25. Qxf5+ Kh8 26.
Rf3 Qb2 27. Re8 Nf6 28. Qxf6+ Qxf6 29. Rxf6
Kg7 30. Rff8 Ne7 31. Na5 h5 32. h4 Rb8 33.
Nc4 b5 34. Ne5 1-0
https://ras44.github.io/blog/2019/01/19/keeping-credentials-secret-with-keyrings-in-r.html
http://www.storybench.org/how-to-build-a-website-with-blogdown-in-r/
Bu bir derlemedir, mümkün mertebe alıntılara linklerle referans vermeye çalıştım.↩︎